带AGV数量约束的柔性作业车间调度问题研究*

doi:10.16731/j.cnki.1671-3133.2025.06.002

现代制造工程 ›› 2025, Vol. 537 ›› Issue (6): 11-21.doi: 10.16731/j.cnki.1671-3133.2025.06.002

• 先进制造系统管理运作 • 上一篇下一篇

带AGV数量约束的柔性作业车间调度问题研究^*

廖雪超^1,2, 向桂宏^1,2, 阮兵³, 田芮利³, 钟实⁴

1 武汉科技大学计算机科学与技术学院,武汉 430065;
2 智能信息处理与实时工业系统湖北省重点实验室,武汉 430065;
3 中国汽车工业工程有限公司,天津 300113;
4 武汉钢铁股份有限公司设备管理部技术室,武汉 430081

收稿日期:2024-08-21 出版日期:2025-06-18 发布日期:2025-07-16
通讯作者: 阮兵,本科,高级工程师,主要研究方向为计算机软件及计算机应用、自动化技术。
作者简介:廖雪超,博士,副教授,主要研究方向为大数据和计算机应用。E-mail:liaoxuechao@wust.edu.cn; 向桂宏,硕士研究生,主要研究方向为智能优化调度。E-mail:2868307592@qq.com
基金资助:
^*国家自然科学基金项目(62176191)

Research on flexible job-shop scheduling problem with AGV quantity constraints

LIAO Xuechao^1,2, XIANG Guihong^1,2, RUAN Bing³, TIAN Ruili³, ZHONG Shi⁴

1 School of Computer Science and Technology,Wuhan University of Science and Technology, Wuhan 430065,China;
2 Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065,China;
3 Automotive Engineering Co., Ltd.,Tianjin 300113,China;
4 Technical Office of Equipment Management Department,Wuhan Iron and Steel Co., Ltd., Wuhan 430081,China

Received:2024-08-21 Online:2025-06-18 Published:2025-07-16

摘要/Abstract

摘要： 在实际工业生产过程中,由于自动导引车(Automated Guided Vehicles,AGVs)资源有限,因此在柔性作业车间调度问题(Flexible Job-shop Scheduling Problem,FJSP)中考虑有限AGV数量约束(FJSP-AGV)的集成问题有重要的研究价值。传统的进化算法容易陷入局部最优,不适用于求解此类复杂程度较高的调度问题。针对以上难点,首先对FJSP-AGV集成问题建立数学模型;然后提出了基于启发式规则引导的改进遗传算法,算法针对不同编码段采用多种交叉、变异方式进化种群,同时在进化过程中作参数自适应调整,并通过启发式规则引导变异进行局部搜索,提高算法跳出局部最优的能力,从而实现系统最大完工时间的最小化。通过在两组中小规模数据集上与其他先进算法的对比分析可知,所提算法的整体求解效果最优。

关键词: 柔性作业车间调度, 自动导引车, 车辆调度, 遗传算法, 启发式规则

Abstract: In the actual industrial production process,due to the limited resources of Automate Guided Vehicles (AGVs), the integrated problem FJSP-AGV comsidering the constraint of alimited number of AGVs in the Flexible Job-shop Scheduling Problem (FJSP) has significant research value. Traditional evolutionary algorithms are easy to fall into local optimum and are not suitable for solving this scheduling problem with high complexity.In light of the aforementioned challenges, it initially established a mathematical model for FJSP-AGV and subsequently proposed an improved genetic algorithm guided by heuristic rules. The algorithm utilized various crossover and mutation methods to evolve the population for different coding segments.Simultaneously,it adjusted parameters adaptively during the evolutionary process and guided mutations through heuristic rules for local search,thereby enhancing the algorithm′s capability to escape local optima and consequently minimize the maximum completion time of the system. Comparison and analysis with other advanced algorithms on two small and medium-sized datasets demonstrated that the algorithm proposed yielded the most comprehensive solving effect.

Key words: Flexible Job-shop Scheduling Problem (FJSP), Automated Guided Vehicles (AGV), vehicle scheduling, Genetic Algorithms (GA), heuristic rules

中图分类号:

TP390

廖雪超, 向桂宏, 阮兵, 田芮利, 钟实. 带AGV数量约束的柔性作业车间调度问题研究^*[J]. 现代制造工程, 2025, 537(6): 11-21.

LIAO Xuechao, XIANG Guihong, RUAN Bing, TIAN Ruili, ZHONG Shi. Research on flexible job-shop scheduling problem with AGV quantity constraints[J]. Modern Manufacturing Engineering, 2025, 537(6): 11-21.

参考文献

[1] HAM A.Transfer-robot task scheduling in flexible job shop[J].Journal of Intelligent Manufacturing,2020,31(7):1-11.
[2] YAO Y J,LIU Q H,LI X Y,et al.A novel MILP model for job shop scheduling problem with mobile robots[J].Robotics and Computer-Integrated Manufacturing,2023,81:102506.
[3] LIM H C,MOON K S.A Two-Phase Iterative Mathematical Programming-Based Heuristic for a Flexible Job Shop Scheduling Problem with Transportation[J].Applied Sciences,2023,13(8):13085215.
[4] EROL R,SAHIN C,BAYKASOGLU A,et al.A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems[J].Applied Soft Computing Journal,2012,12(6):1720-1732.
[5] 孙爱红,雷琦,宋豫川,等.基于深度强化学习求解作业车间机器与AGV联合调度问题[J].控制与决策,2024,39(1):253-262.
[6] ZHENG Y,XIAO Y,SEO Y.A tabu search algorithm for simultaneous machine/AGV scheduling problem[J].International Journal of Production Research,2014,52(19):5748-5763.
[7] CHAUDHRY I,RAFIQUE A,ELBADAWI I,et,al.Integrated scheduling of machines and Automated Guided Vehicles (AGVs) in flexible job shop environment using genetic algorithms[J]. International Journal of Industrial Engineering Computations,2022,13(3):343-362.
[8] YAN J,LIU Z,ZHANG C,et al.Research on flexible job shop scheduling under finite transportation conditions for digital twin workshop[J].Robotics and Computer-Integrated Manufacturing,2021,72:102198.
[9] FONTES M M B D,HOMAYOUNI M S.Joint production and transportation scheduling in flexible manufacturing systems[J].Journal of Global Optimization,2019,74(4):879-908.
[10] HOMAYOUNI M S,FONTES M M B D.Production and transport scheduling in flexible job shop manufacturing systems[J].Journal of Global Optimization,2021,79(2):463-502.
[11] HAN X,CHENG W,MENG L,et al.A dual population collaborative genetic algorithm for solving flexible job shop scheduling problem with AGV[J].Swarm and Evolutionary Computation,2024,86:101538.
[12] LI Wenlong,LI Huan,WANG Yuting,et al.Optimizing flexible job shop scheduling with automated guided vehicles using a multi-strategy-driven genetic algorithm[J].Egyptian Informatics Journal,2024,25:100437.
[13] DEROUSSI L,GOURGAND M,TCHERNEV N. A simple metaheuristic approach to the simultaneous scheduling of machines and automated guided vehicles[J].International Journal of Production Research,2007,46(8):2143-2164.
[14] KUMAR S V M,JANARDHANA R,RAO P S C. Simultaneous scheduling of machines and vehicles in an FMS environment with alternative routing[J].The International Journal of Advanced Manufacturing Technology,2011,53(1/2/3/4):339-351.
[15] PAN Z,WANG L,ZHENG J,et al.A learning-based multi-population evolutionary optimization for flexible job shop scheduling problem with finite transportation resources[J].IEEE Transactions on Evolutionary Computation,2022,27(6):1590-1603.
[16] 陈魁,毕利,王文雅.柔性作业车间AGV与机器双资源集成调度研究[J].系统仿真学报,2022,34(3):461-469.
[17] 胡晓阳,姚锡凡,黄鹏,等.改进迭代局部搜索算法求解多AGV柔性作业车间调度问题[J].计算机集成制造系统,2022,28(7):2198-2212.
[18] HOMAYOUNI S M,FONTES D B M M,GONCALVES J F. A multistart biased random key genetic algorithm for the flexible job shop scheduling problem with transportation[J].International Transactions in Operational Research,2020,30(2):688-716.
[19] FONTES D B M M,HOMAYOUNI S M,GONÇALVES J F.A hybrid particle swarm optimization and simulated ann-
ealing algorithm for the job shop scheduling problem with transport resources[J].European Journal of Operational Research,2023,306(3):1140-1157.
[20] TANG H,WANG J,YANG Z. Research on the Integration and Scheduling of AGVs and Machines in Sample Testing Laboratory[J].IEEE Access,2023(11):70652-70667.
[21] WEN X,FU Y,YANG W,et al.An effective hybrid algorithm for joint scheduling of machines and AGVs in flexible job shop[J].Measurement and Control,2023,56(9/10):1582-1598.
[22] DEROUSSI L,NORRE S. Simultaneous scheduling of machines and vehicles for the flexible job shop problem[C]//International conference on metaheuristics and nature inspired computing.Tunisia:Djerba Island,2010.
[23] HUANG X,YANG L. A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time[J].International Journal of Intelligent Computing and Cybernetics,2019,12(2):154-174.

带AGV数量约束的柔性作业车间调度问题研究^*

Research on flexible job-shop scheduling problem with AGV quantity constraints

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	李建儒, 龚堰珏, 赵罘. 基于混合遗传粒子群算法的机器人关节空间轨迹规划^*[J]. 现代制造工程, 2025, 537(6): 84-91.
[2]	赵倩, 石宇强. 融合改进A^*算法与DWA的AGV实时路径规划及避障研究[J]. 现代制造工程, 2025, 536(5): 91-98.
[3]	陈德海, 田森焱, 李志军, 王海峰. 基于解析计算与遗传算法的永磁同步电机偏心磁极优化设计^*[J]. 现代制造工程, 2025, 536(5): 153-160.
[4]	刘晋飞, 刘乙涵, 陈明, 黄华. 基于改进IGA的多品种变批量智能车间调度^*[J]. 现代制造工程, 2025, 535(4): 1-10.
[5]	王春, 张朝阳, 吉卫喜, 卢璟钰. 考虑设备退化的离散车间生产排程优化研究^*[J]. 现代制造工程, 2025, 534(3): 31-40.
[6]	刘亮, 贺禹铭, 祁思远. 基于数字孪生仿真的柔性作业车间调度优化研究^*[J]. 现代制造工程, 2025, 534(3): 41-51.
[7]	黄开启, 胡智强. 矿用自卸车互联油气悬架参数多目标优化[J]. 现代制造工程, 2025, 534(3): 124-131.
[8]	杨丹, 舒先涛, 余震, 鲁光涛, 纪松霖, 王家兵. 深度强化学习求解动态柔性作业车间调度问题^*[J]. 现代制造工程, 2025, 533(2): 10-16.
[9]	李俊聪, 黄翔, 曾京, 汪群生. 高速列车蛇行稳定性自适应控制策略研究^*[J]. 现代制造工程, 2025, 533(2): 61-68.
[10]	霍俊杰, 王志坚. 采用新编码GA的工艺规划与车间调度集成优化^*[J]. 现代制造工程, 2024, 528(9): 25-33.
[11]	杨兴臣, 苌道方. 有限缓冲区下多汽车工厂协同混流排产研究[J]. 现代制造工程, 2024, 527(8): 9-18.
[12]	张苗苗, 侯荣国, 吕哲, 王龙庆, 石广行, 王中庆. 基于GA-GRNN的AWJ强化3D打印AlSi10Mg表面性能实验研究^*[J]. 现代制造工程, 2024, 526(7): 35-41.
[13]	裴红蕾. 具有串并行异类工序约束的多柔性车间联合调度^*[J]. 现代制造工程, 2024, 525(6): 15-21.
[14]	袁东维, 凤飞龙. 工业互联网环境下基于协作度的制造服务组合推荐方法^*[J]. 现代制造工程, 2024, 525(6): 69-74.
[15]	张殷晨, 左鹏奇, 王逸飞, 林楠, 谢润, 金立军. 基于遗传算法的家电智能生产线分布式资源调度算法设计^*[J]. 现代制造工程, 2024, 524(5): 31-38.